AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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2,981 papers
The results show that pipelined fixed-point FPGA architectures are a good solution to speed up the reinforcement learning process in resource-limited embedded systems.
The Synergistic Policy network for Edge Resource Control (SPERC) is presented, a novel deep reinforcement learning framework designed to solve this joint problem of ensuring performance through appropriate computational resource allocation and minimizing storage costs through image layer sharing.
CK Vision's 13MP MIPI CSI camera modules represent the pinnacle of high-resolution embedded imaging, combining optoelectronic precision with flexible hardware interfaces. Engineered to meet the technical excellence demanded by the ever-evolving field of machine vision, our 13.2 mp mipi camera module portfolio delivers...
Kolmogorov-Arnold Networks (KANs) replace the fixed activation functions and linear weights of Multi-Layer Perceptrons (MLPs) with learnable univariate functions on network edges, offering improved interpretability and, in some settings, competitive parameter efficiency. While the approximation properties of KANs have...
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This paper shows that the proposed method ensures a bounded Byzantine influence on both distillation gradients and individual client private gradients after cross-modality fusion, thereby enabling stable local optimization for honest clients under Byzantine distillation.
An AI-for-science approach to linking neural connectivity to computation is suggested and a conditional generative latent framework is proposed that encodes connectome graphs into a compact structural space while using available node-level conditions to guide reconstruction and generation.
A controlled measurement study of self-hosted LLM inference across edge and near-edge deployment nodes: an NVIDIA Jetson AGX Orin and a near-edge server with CPU-only and GPU-enabled inference modes, highlighting that compute-side inference metrics alone can lead to suboptimal placement for latency-sensitive interactiv...
Deployment of Large Language Models (LLMs) on memory-constrained edge devices relies heavily on aggressive post-training quantization. However, evaluating these models is largely based on zero-shot task accuracy, which depends solely on argmax predictions and is insensitive to changes in the underlying predictive distr...
Mobile edge computing (MEC) accelerates Internet of Things (IoT) applications by caching content near end users. However, cached data remains vulnerable to corruption, misleading applications, and eroding user trust. Traditional centralized edge data integrity verification (EDIV) methods adopt challenge-response protoc...
This paper proposes SEELE, a Lyapunov-guided online control algorithm that represents accumulated budget pressure with two virtual queues and converts the long-term constrained problem into lightweight per-slot decisions, and provides steady-state QoE statistically comparable to a pretrained PPO policy while significan...
In this review, recent studies on TRNGs based on emerging material-based semiconductor devices are examined from the perspectives of entropy sources, device structures, randomness validation, and wearable/flexible extensions to provide a useful framework for future research on hardware security devices.
Le contrôle qualité en temps réel des pièces composites dans l'industrie aéronautique représente un enjeu majeur pour garantir l'intégrité structurale des composants tout en maintenant des cadences de production élevées. Cet article présente un système modulaire open-source combinant vision industrielle et Line Scan Th...
Host Sowbhagya Suresh shares some of the latest news and cutting-edge research from across Microsoft—from understanding the challenges facing coding agents to uncovering how people use AI at work and improving extended-range weather forecasting. The post Microsoft Research Focus – Edition #97 appeared first on Microsoft Research.
Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.
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For decades, a fundamental question about the difficulty of computing in high-dimensional spaces has resisted proof. New research resolves this question, removing a major theoretical obstacle to understanding the efficiency of randomized algorithms used across science and computing. The Kannan–Lovász–Simonovits (KLS) conjecture was proposed in 1995. It asks whether, for a broad class of high-dimensional […] The post Solving a 31-year-old grand challenge in mathematics – the KLS conjecture appear…
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
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